Data_Sheet_1_Marker Selection in Multivariate Genomic Prediction Improves Accuracy of Low Heritability Traits.PDF

Multivariate analysis using mixed models allows for the exploration of genetic correlations between traits. Additionally, the transition to a genomic based approach is simplified by substituting classic pedigrees with a marker-based relationship matrix. It also enables the investigation of correlated responses to selection, trait integration and modularity in different kinds of populations. This study investigated a strategy for the construction of a marker-based relationship matrix that prioritized markers using Partial Least Squares. The efficiency of this strategy was found to depend on the correlation structure between investigated traits. In terms of accuracy, we found no benefit of this strategy compared with the all-marker-based multivariate model for the primary trait of diameter at breast height (DBH) in a radiata pine (Pinus radiata) population, possibly due to the presence of strong and well-estimated correlation with other highly heritable traits. Conversely, we did see benefit in a shining gum (Eucalyptus nitens) population, where the primary trait had low or only moderate genetic correlation with other low/moderately heritable traits. Marker selection in multivariate analysis can therefore be an efficient strategy to improve prediction accuracy for low heritability traits due to improved precision in poorly estimated low/moderate genetic correlations. Additionally, our study identified the genetic diversity as a factor contributing to the efficiency of marker selection in multivariate approaches due to higher precision of genetic correlation estimates.

采用混合模型（mixed models）开展多变量分析，可探索不同性状间的遗传相关（genetic correlations）。以基于标记的亲缘关系矩阵（marker-based relationship matrix）替代经典系谱，不仅可简化向基于基因组的研究方法的转型过程，还可用于探究不同群体中选择的相关响应、性状整合与模块化特性。本研究针对一种基于标记的亲缘关系矩阵构建策略展开探究，该策略通过偏最小二乘（Partial Least Squares）对分子标记进行优先级筛选。研究发现，该策略的有效性取决于所研究性状间的相关结构。就预测精度而言，在辐射松（Pinus radiata）群体的核心性状——胸径（diameter at breast height, DBH）的分析中，该策略相较全标记多变量模型并无优势，这可能是由于该性状与其他高遗传力性状间存在较强且估算准确的遗传相关；与之相反，在亮果桉（Eucalyptus nitens）群体中，该策略展现出显著优势，该群体的核心性状与其他低或中等遗传力性状间仅存在较弱或中等程度的遗传相关。因此，多变量分析中的标记筛选策略可有效提升低遗传力性状的预测精度，这得益于对低/中等遗传相关的估算精度提升。此外，本研究还发现，遗传多样性可提升遗传相关的估算精度，是影响多变量分析中标记筛选策略有效性的重要因素。